Research map / Video / Video segmentation and tracking
Event-Based High Dynamic Range Image and Very High Frame Rate Video Generation Using Conditional Generative Adversarial Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SBE instead of SBT for event stacking | Event-to-image cGAN · Real-world sequences | Reconstruction quality | Helped |
| Use three frames per event stack instead of one (ablation) | Event-to-image cGAN with SBE · ESIM | PSNR (dB): 20.51±2.86 -> 24.87±3.15 | Helped |
| Use three frames per event stack instead of one (ablation) | Event-to-image cGAN with SBE · ESIM | FSIM: 0.81±0.09 -> 0.87±0.06 | Helped |
| Use three frames per event stack instead of one (ablation) | Event-to-image cGAN with SBE · ESIM | SSIM: 0.67±0.20 -> 0.79±0.12 | Helped |
| Use SBE rather than SBT | Event-to-image cGAN · Real-world sequences | Reconstruction robustness | Helped |
Closest papers by meaning
- E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning (AAAI 2024 · similarity 0.89)
- EventSplat: 3D Gaussian Splatting from Moving Event Cameras for Real-time Rendering (CVPR 2025 · similarity 0.87)
- Leveraging Asynchronous Spiking Neural Networks for Ultra Efficient Event-Based Visual Processing (AAAI 2025 · similarity 0.87)
- LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising (CVPR 2024 · similarity 0.86)
- V2V: Scaling Event-Based Vision through Efficient Video-to-Voxel Simulation (NeurIPS 2025 · similarity 0.86)
- Event-3DGS: Event-based 3D Reconstruction Using 3D Gaussian Splatting (NeurIPS 2024 · similarity 0.86)
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